An organism's genome must be accurately replicated and packaged into a new cell to faithfully propagate genetic material from one generation to the next. Aneuploidy, the state of having too many or too few chromosomes, is a leading cause of developmental defects (e.g. Down's syndrome), miscarriages, and failed pregnancies using assisted reproductive technology; therefore, reproductive success is completely dependent on accurate chromosome segregation. During meiosis, accurate chromosome segregation is ensured by using recombination to create COs (COs) between homologous chromosomes. Recombination is initiated by a DNA double- stranded break (DSB) that can be repaired either as a CO or a noncrossover (NCO) through a series of structural intermediates. The bifurcation in repair pathways is thought to occur early, likely at or before the time DSBs are formed, and the choice in repair outcome is a critical regulatory point in establishing the distribution of crossovers across the genome. The current proposal seeks to understand how repair outcome is decided and its role in determining the recombination landscape through three different approaches. First, the signatures of different types of recombination events (heteroduplex DNA) will be mapped across an entire metazoan genome using a genetic trick that allows the sequencing of only the maternal haploid genome. Secondly, a CRISPR-Cas9 based whole genome DSB repair assay is described that will allow repair outcome to be easily monitored and manipulated. And lastly, the roles of several factors in deciding and communicating repair outcome will be tested, including chromosome environment (location, heterochromatin, chromatin marks and chromosome compaction), the distance between DSBs, and recombination surveillance mechanisms. Each of these three approaches requires quantitative and computational skill sets that need to be developed by the candidate during the mentored phase in order for success as an independent scientist. A series of training activities is proposed included courses in computational biology and statistics, seminar attendance in the Curriculum for Bioinformatics and Computational Biology, individual training with the co-mentor, an expert mentoring panel, and immersion in the Computational Biology research groups at the sponsoring institution.